Robust optimization of medical waste recycling network in uncertain environment

Aiming at the uncertainty of medical waste recycling network, with the quantity and transportation cost of medical waste as the key variables, a multi-objective nonlinear integer programming model with multiple uncertain parameters was constructed, and robust optimization was introduced to deal with the uncertain factors. Multi-objective particle swarm optimization (MOPSO) and genetic algorithm (GA) were combined to solve the model. The outer GA was responsible for location decision, and the inner MOPSO was responsible for distribution path optimization based on location selection results. A domestic city was selected as the empirical object for the simulation. The results show that compared with the traditional genetic algorithm, the proposed algorithm reduces the total cost by 10.37%, the total risk by 1.86% and the workload deviation by 50.18%; Sensitivity analysis proves that the uncertainty of medical waste volume has more significant influence on the objective function. The proposed mode can help the decision makers adjust the uncertain parameters according to the risk appetite to obtain the best medical waste recycling network optimization scheme, which provides some reference for further study of medical waste recycling.